Abstract Kernel Class
Abstract class that cannot be constructed directly.
Returns error. Abstract classes cannot be constructed directly.
distr6::Distribution -> Kernel
packageDeprecated, use $packages instead.
packagesPackages required to be installed in order to construct the distribution.
new()
Creates a new instance of this R6 class.
Kernel$new(decorators = NULL, support = Interval$new(-1, 1))
decorators(character())
Decorators to add to the distribution during construction.
support[set6::Set]
Support of the distribution.
mode()
Calculates the mode of the distribution.
Kernel$mode(which = "all")
which(character(1) | numeric(1)
Ignored if distribution is unimodal. Otherwise "all" returns all modes, otherwise specifies
which mode to return.
mean()
Calculates the mean (expectation) of the distribution.
Kernel$mean(...)
...Unused.
median()
Calculates the median of the distribution.
Kernel$median()
pdfSquared2Norm()
The squared 2-norm of the pdf is defined by
\int_a^b (f_X(u))^2 du
where X is the Distribution, f_X is its pdf and a, b are the distribution support limits.
Kernel$pdfSquared2Norm(x = 0, upper = Inf)
x(numeric(1))
Amount to shift the result.
upper(numeric(1))
Upper limit of the integral.
cdfSquared2Norm()
The squared 2-norm of the cdf is defined by
\int_a^b (F_X(u))^2 du
where X is the Distribution, F_X is its pdf and a, b are the distribution support limits.
Kernel$cdfSquared2Norm(x = 0, upper = Inf)
x(numeric(1))
Amount to shift the result.
upper(numeric(1))
Upper limit of the integral.
skewness()
The skewness of a distribution is defined by the third standardised moment,
sk_X = E_X[((x - μ)/σ)^3]
where E_X is the expectation of distribution X, μ is the mean of the distribution and σ is the standard deviation of the distribution.
Kernel$skewness(...)
...Unused.
clone()
The objects of this class are cloneable with this method.
Kernel$clone(deep = FALSE)
deepWhether to make a deep clone.
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